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Starts 4 July 2025 17:41

Ends 4 July 2025

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Transforming Financial Decision Intelligence Through Advanced Neural Networks

Uncover how advanced neural networks are reshaping the landscape of financial decision intelligence, offering groundbreaking improvements in areas such as risk assessment, fraud detection, and investment strategy formulation. Dive into the development of sophisticated anomaly detection frameworks that optimize financial outcomes and safeguard.
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Overview

Uncover how advanced neural networks are reshaping the landscape of financial decision intelligence, offering groundbreaking improvements in areas such as risk assessment, fraud detection, and investment strategy formulation. Dive into the development of sophisticated anomaly detection frameworks that optimize financial outcomes and safeguard assets.

Syllabus

  • Introduction to Financial Decision Intelligence
  • Overview of Financial Decision-Making Processes
    Importance of AI in Finance
    Historical Perspective of AI in Financial Services
  • Fundamentals of Neural Networks
  • Basics of Neural Network Architecture
    Key Concepts: Neurons, Layers, Activation Functions
    Types of Neural Networks: Feedforward, Convolutional, Recurrent
  • Advanced Neural Network Architectures
  • Deep Learning Models
    Transfer Learning and Its Applications in Finance
    Generative Adversarial Networks (GANs)
  • Neural Networks in Risk Assessment
  • Credit Scoring Models
    Market Risk Analysis
    Stress Testing and Scenario Analysis
  • Fraud Detection Using Neural Networks
  • Pattern Recognition and Anomaly Detection
    Real-Time Transaction Monitoring
    Supervised vs. Unsupervised Approaches
  • Enhancing Investment Strategies with Neural Networks
  • Algorithmic Trading Models
    Portfolio Management Optimization
    Sentiment Analysis and its Impact on Investment
  • Anomaly Detection Frameworks
  • Techniques for Identifying Unusual Patterns
    Use of Autoencoders and RNNs in Anomaly Detection
    Case Studies of Anomaly Detection in Financial Markets
  • Ethical and Regulatory Considerations
  • Ethical AI in Finance
    Understanding Regulatory Compliance and GDPR
    Bias and Fairness in AI Decision-Making
  • Case Studies and Real-World Applications
  • Case Study: Neural Networks in Hedge Funds
    Application: Fraud Detection in Real-Time Transactions
    Emerging Trends in AI and Financial Technology
  • Practical Implementation and Tools
  • Hands-on Session: Building Neural Network Models with Python
    Introduction to Frameworks: TensorFlow, PyTorch
    Project: Developing a Neural Network for Financial Prediction
  • Conclusion and Future Directions
  • Summary of Key Learnings
    The Future of AI and Neural Networks in Finance
    Opportunities for Further Learning and Development

Subjects

Computer Science